Fully Homomorphic Encryption

What is FHE?

Fully Homomorphic Encryption (FHE) is a cryptographic technique that allows complex computations to be performed on encrypted data without needing to decrypt it. This means that sensitive data can be processed securely, preserving privacy and ensuring that only encrypted results are visible throughout the computation process. FHE is a powerful tool for enhancing data security, particularly in cloud computing and data sharing scenarios.

Cluster Protocol's Utilization of FHE:

Within the Cluster Protocol ecosystem, FHE is leveraged to safeguard the privacy of data as it undergoes distributed AI training and validation, ensuring that participants can contribute to and benefit from AI models without exposing sensitive information.

AI Training Architecture with FHE

Publisher Initialization: The central entity, or publisher, is responsible for initializing and distributing AI training tasks. It determines the structure and parameters of the AI model to be trained and encrypts the training task using FHE to maintain data privacy throughout the process.

Model Initialization: $FHE(Model i ​ )→Trainer i ​$

Trainers Execution:

• Each trainer receives the encrypted training task from the publisher.The trainers use their respective local datasets, which remain encrypted throughout the process to train the local models. FHE allows the training process to perform the necessary computations on encrypted data, such as weights adjustments and gradient calculations, without decrypting the data, thus preserving the privacy of the dataset.

$FHE(Compute(Local Data i ​ ))$

Local Model Generation:

• Upon completing the training task, each trainer generates a local model. To validate the integrity of the training process and the accuracy of the model, the PoC layer verify the training steps and the resultant model without revealing the underlying data or model specifics.

Local Model: $FHE(Train(Local Data i ​ ))$

Secure Aggregation Protocol:

• Once all trainers have completed their tasks and generated their respective local models with FHE proofs, a secure sum protocol is initiated. Cluster protocol securely aggregates the encrypted models to compute a global model. The aggregation is done homomorphically to ensure that the resultant global model benefits from the learning of all local models without compromising the data privacy.

$Global Model=∑ i=1 n ​ FHE(Local Model i ​ )$

Final Output:

• The final output is an encrypted global model, which has been trained collaboratively by all trainers in the network without exposing any individual's data.

• Decrypted Global Model = FHE −1 (Global Model)

The Cluster Protocol uses FHE architecture to maintain the privacy of individual datasets while allowing computation on encrypted data to create a collaboratively trained global model.

Why choose FHE over Zero Knowledge Proofs?

Fully Homomorphic Encryption (FHE) enables computations on encrypted data while maintaining privacy, whereas Zero-Knowledge Proofs (zk-SNARKs and zk-STARKs) offer efficient and secure ways to verify computations without revealing sensitive information. The choice between FHE and ZKPs depends on specific use case requirements, with FHE focusing on secure computation on encrypted data and ZKPs emphasizing privacy-preserving verification mechanisms.